validating the academic self-regulated learning scale with the motivated strategies for learning...

56 The International Journal of Educational and Psychological Assessment May 2011, Vol. 7(2)

© 2011 Time Taylor Academic Journals ISSN 2094-0734

Validating the Academic Self-regulated Learning Scale with the Motivated Strategies

for Learning Questionnaire (MSLQ) and Learning and Study Strategies Inventory

(LASSI)

Carlo Magno

De La Salle University, Manila, Philippines

Abstract

The present study further established the construct validity of the Academic Self-regulated

Learning Scale (A-SRL-S, Magno, 2010) through its functional correlation with the

Motivated Strategies for Learning Questionnaire (MSLQ) and Learning and Study

Strategies Inventory (LASSI). The three questionnaires were administered to 755 college

students from different universities in the National Capital Region in the Philippines. All

subscales of the three instruments had significant intercorrelations (p<.001). Three

measurement models, using Confirmatory Factor Analysis were tested to determine which

best explains the construction of the A-SRL-S. A two factor model where the A-SRL-S was

combined with MLSQ with LASSI on a separate factor turned to have a bad fit

(2

=2648.02, df=89, RMSEA=.26, SRMR=.20, AIC=3.39, SBC=3.78, BCCVI=3.60).

Another two-factor model where A-SRL-S was combined with LASSI with MSLQ this time

on a separate factor improved its fit as compared to the first model (2

=1052.99, df=89,

RMSEA=.13, SRMR=.09, AIC=1.47, SBC=1.66, BCCVI=1.48). The last three-factor

model where A-SRL-S, MSLQ, and LASSI are structured as separate correlated factors

turned to have the best fit (2

=473.47, df=87, RMSEA=.08, SRMR=.04, AIC=.71,

SBC=.92, BCCVI=.71). Implications about the usefulness and validity of the A-SRL-S in

research were discussed.

Keywords: self-regulation, Academic Self-regulated Learning Scale, Motivated Strategies for

Learning Questionnaire, Learning and Study Strategies Inventory

Introduction

When self-regulation is measured in quantitative studies, it requires the use

of a direct instrument that captures its conceptualizations, dispositions, and skills.

Researchers find it important to assess self-regulation among learners because they

are concerned at determining what thinking processes and strategies does students

use when engaged in a cognitive task such as memorizing, problem solving, focusing

one’s attention on a stimuli, and answering tests. Having determed the level of self-

regulation of a learner allows researchers to predict how well students can succeed

in a task or achieve in an academic pursuit. Learners and students who are

academically self-regulated are independent in their studies, diligent in listening

inside the classroom, focused on doing their task inside the classroom, gets high

scores in tests, able to recall teacher’s instruction and facts lectured in class, and

submits quality work (Magno, 2009). There are even several studies that established

the successful outcomes and consequences of self-regulation (e. g., Blakey &

Spencer, 1990; Collins, 1982; Corsale & Ornstein, 1980; Kluwe, 1982; Lopez,

Little, Oettingen, Baltes, 1998; Rock, 2005; Schneider, 1985).



There are several direct measures of self-regulation that were developed

such as the use of the structured interviews like the Self-regualtion Interview

Schedule (SRLIS), Questionnaires, teacher judgments, think aloud techniques,

error detection tasks, trace methodologies, and observation of performance (see

Winne & Perry, 2005; Zimmerman, 2008). One instrument that was recently

developed to measure self-regulation in an academic context is the Academic Self-

regulated Learning Scale (A-SRL-S, Magno, 2010). The A-SRL-S is a scale where

items are classified under seven factors of self-regulation: Memory strategy, goal

setting, self-evaluation, seeking assistance, environmental structuring, learning

responsibility, and planning and organizing. These factors were first uncovered

using principal components analysis that classified a seven factor solution. Then the

factor solution was confirmed in another sample (n=309) by testing a seven factor

model using Confirmatory Factor Analysis (CFA). The CFA confirmed the seven

factor model with adequate fit (χ2

=332.07, df=1409, RMS=.07, RMSEA=.06,

GFI=.91, and NFI=.89). Aside from these findings, the items showed high internal

consistency using Cronbach’s alpha. High Cronbach’s alpha values were still

obtained even when the items were separated by factor.

What is new in the analysis of the A-SRL-S is the precision determined

using the Item Response Theory that cannot be determined using a Classical Test

Theory Approach. Through the IRT, the item functioning of the A-SRL-S was

further investigated. Specifically, a Graded Response Model (GRM) was used to

test the calibration of the items with polychotomous responses. The GRM features

estimation of an ogive curve for every category of the scale used for each item. In a

regular IRT model for tests’ with right and wrong answer, the probability of

answering an item correct given the ability of respondents is estimated with an ogive

curve known as Item Characteristic Curve (ICC). In a GRM, the estimates of each

items’ probability of response for each scale (like a Lickert scale) is represented by

ogive curves. The results of the GRM analysis made by Magno (2010) showed that

the step calibrations for each factor were adequate where values were monotonicall

increasing from negative values to positive values. Lower scale categories generally

had negative estimates while higher scale categories reached a positive value. All

items also showed adequate fit where mean square values for each item ranged

within 0.8 to 1.2. The Test Information Function (TIF) covers five standard

deviations below and on top of the 0 which covers a large spectrum of behavior.

This showed the tool’s precision in measuring self-regulation. Since the IRT

features independent calibration for the items and ability, the obtained item and

person reliability for each scale was also very high (see Magno, 2010).

To further validate the construction of self-regulation, the A-SRL-S needs to

be studied with other measures of self-regulation. The two most common measures

of self-regulated learning in literature reviews within the field of education and

psychology are the Motivated Strategies for Learning Questionnaire (MSLQ,

Pintrich, Smith, Garcia, & McKeachie, 1991, 1993) and the Learning and Study

trategies Inventory (LASSI, Weinstein, Palmer, & Schulte, 1987). These two

instruments are commonly used in studies that involve the measurement self-

regulation. The selection of the LASSI and MSLQ to validate self-regulation



behavior is based on the classification of common scales for self-regulation by

Olaussen and Braten (1999).

The MSLQ was designed by Pintrich, Smith, Garcia, and McKeachie in

1991 to assess college students on two aspects: Their motivational orientation and

use of different strategies. The motivational orientation consists of measurement for

values, expectancies, and affective components. The values include intrinsic goal

orientation, extrinsic goal orientation, and task value. The expectancies are

composed of control of learning beliefs and self-efficacy for learning and

performance. The affective is composed of test anxiety. The learning strategies

include cognitive and metacognitive and resource management strategies. The

cognitive and metacognitive strategies are composed of rehearsal, elaboration,

organization, critical thinking, and metacognitive self-regulation. The resource

management is composed of time and study environment, effort regulation, peer

learning, and help seeking. The scale correlations were adequate and the factors

were also confirmed with adequate fit (2

/df=3.49, GFI=.77, RMR=.07) for the

motivation part and use of strategies (2

/df=2.26, GFI=.78, RMR=.08). There are

also several studies that tested the reliability and validity of the MSLQ.

There is strong evidence that the MSLQ measures self-regulation. There

are numerous studies that use this instrument to assess self-regulation behaviors

from domain-general to domain specific areas. Example of studies using the MSLQ

for domain-specific subject areas are conducted by Malmivuori (2006) for

mathematics, Lee, Lim, and Grabowski (2009) and Yoon (2009) for science, Joo,

Bong, and Choi (2000) for web-based instruction, Chen (2002) for an information

systems course, Moos and Azevedo (2006) for a hypermedia learning tasks, Yusri

and Rahimi (2008) for language, and Mullen (2006) for a nursing program. The

other studies used MSLQ to measure self-regulation as domain general skills such

as Guvenc (2010), Sungur and Tekkaya (2006), Graner (2009), Eden (2009), Kesici

and Erdogan (2009), Kitsantas, Winsler, and Huie (2008), Bembenutty (2007),

Ertmer, Newby and MacDougal (1996), and Moos and Azevedo (2006).

The LASSI was devised by Weinstein and Palmer in 1990 to assess

students’ awareness about and use of learning and study strategies. These strategies

are said to be related to skill, will and self-regulation components of strategic

learning. The tool is intended to help students develop awareness of the strengths

and weaknesses in studying. The LASSI measures general domains on study skill,

will, and self-regulation. The three domains of study skills are information

processing, selecting main ideas, and test strategies. The subscales of will are

anxiety, attitude, and motivation. The self-regulation includes concentration, self-

testing, study aids, and time management. Very high coefficient alphas were

obtained for each of the scales. In the initial development of the LASSI, test-retest

reliability with an interval of 3 to 4 weeks was conducted and obtained a coefficient

of .88 for the whole scale (Weinstein & Palmer, 1990). Adequate scale correlations

were also obtained. The LASSI scales were validated by comparing it with

measures of similar learning behaviors and measures of ability (Eldrege, 1990;

Schutz, 1997).

There are also several studies that used the LASSI to measure self-

regulation. Dembo (2001) recommends the use of the LASSI when structuring a



course to develop self-regulation behaviors of students. Assessing self-regulation

through the LASSI helps to teach students to become better learners. The LASSI

was used to assess students’ self-regulation with learning disabilities (Abrue-Ellis,

Ellis, & Hayes, 2009; Kirby, Silvestri, Allingham, Parilla, & La Fave, 2008). Other

studies focused on determining self-regulatory outcomes (Downing, Chan,

Downing, Kwong, & Lam, 2008; Hamman, 1998; Sizoo, Agusa, & Iskat, 2005;

Wadsworth, Husman, Duggan, & Peninton, 2007).

Given that the MSLQ and LASSI are strong indicators of self-regulatory

functioning, the present study established the construct validity of the A-SRL-S with

these two other measures. Construct validity can be established by correlating a new

scale with similar earlier scales. The procedure approximates the validity of a new

scale with the same general area of behavior as other tests are designed (Anastasi &

Urbina, 2002). Construct validation of the A-SRL-S allows to generalize in a

broader class of measures that legitimately employ the same construct such as the

MSLQ and LASSI (Nunnaly & Bernstein, 1994). More specifically, the present

study established a measurement model where the A-SRL-S together with the

MSLQ and LASSI are structured as latent factors that are correlated (common

factor model). The structured common factor model was assessed whether there

are adequate fit and significant correlations to support for the construct validation of

the A-SRL-S.

Method

Participants

The participants in the study are 755 college students from different

universities in the National Capital Region of the Philippines. These students were

all enrolled in a degree course who is already taking up their major courses. In the

Philippines major courses are taken from second year college until the last year.

There is much evidence of self-regulation behavior because these students are

already experienced several academic tasks and requirements in school.

Instruments

Academic Self-Regulated Learning Scale (A-SRL-S). The A-SRL-S was

developed by Magno (2010) to measure self-regulation of college students that is

within the context of their learning in higher education. Each item is responded by

a four-point Lickert scale (Strongly agree, agree, disagree, and strongly disagree).

The scale is composed of seven factors: Memory strategy (14 items), goal-setting (5

items), self-evaluation (12 items), seeking assistance (8 items), environmental

structuring (5 items), learning responsibility (5 items), and planning and organizing

(5 items). The seven factors were uncovered using an initial principal components

analysis with varimax rotation. Uisng another sample, the seven factor structure was

confirmed using Confirmatory Factor Analysis (CFA) and adequate fit was achieved

(χ2

=332.07, df=1409, RMS=.07, RMSEA=.06, GFI=.91, and NFI=.89). There is

evidence of convergent validity where all seven factors were highly intercorrelated.



High interbal consistencies were also attained for each factor (.73 to .87). Using an

IRT Graded Response Model, the scale showed appropriate step calibration where

the responses are monotonically increasing. The Test Information Function curve

showed precision for the overall instrument. Almost all items showed to have good

fit and the few items that did not fit the GRM were revised.

Motivated Strategies for Learning Questionnaire (MSLQ). The Motivated

Strategies for Learning Questionnaire by Pintrich, Smith, Garcia, and McKeachie

(1991) was used as another measure for self-regulation. The questionnaire is

composed of two sections: The motivation and learning strategy section. The

motivation assesses student values (intrinsic and extrinsic goal orientation and task

value), expectancies (control of learning beliefs, self-efficacy for learning and

performance), and affective beliefs (test anxiety). The learning strategies section

assesses cognitive and metacognitive strategies (rehearsal, elaboration, organization,

critical thinking, metacognitive, and self-regulation) and resource management

strategies (time and study environment, effort regulation, peer and learning help

seeking). All items are responded using a seven-point Likert scale (from 1 – Not at

all true of me to 7 – Very true of me). In general, if students score above three on

the questionnaire, then it means that they are using effective learning strategies.

However, students who score below three mean that they are not using effective

learning strategies (Pintrich, Smith, Garcia, & McKeachie, 1991). The scale is valid

having a significant relationship with all the factors being assessed. It was shown in

the confirmatory factor analysis that the learning strategies are under one latent

factor. Furthermore, the scale is reliable having a Cronbach's Alpha value ranging

from .52 to .93. The Cronbach’s Alpha was recomputed from the scores of

students in the sample.

Learning and Study Strategies Inventory (LASSI). The short version of the

LASSI (with a 5-point scale and 77 items) was used which is a prescriptive and

diagnostic assessment of “student’s awareness” about the use of learning and study

strategies. The three components cover: (1) Skill - learning strategies, skills and

thought processes that help prepare and demonstrate new knowledge on tests or

other evaluative procedures (subscales include information processing, selecting

main ideas, and test strategies), (2) Will - worry to academic performance,

receptivity to learning new information, attitudes and interest in college, diligence,

self-discipline, and willingness to exert the effort necessary to successfully complete

academic requirements (subscales include anxiety, attitude, and motivation), and (3)

Self- Regulation - manage, or self-regulate and control, the whole learning process

through time management, maintaining concentration, checking learning demands,

and using study aids (subscales include concentration, self-testing, study aids, and

time management) (Weinstein & Palmer, 2002). Participants answered the learning

and study strategies inventory on how often they do the given case/scenario through

the response format “not at all like me, not very much like me, somewhat like me,

fairly much like me, and very much like me.” The reliability of LASSI indicates a

Cronbach’s Alpha of .84, .89, and .80 for Information Processing, Selecting Main

Ideas and Test Strategies scales for the “Skill” component respectively. For the



scales of the “Will” component, Anxiety, Attitude and Motivation indicate a

Cronbach’s Alpha score of .87, .77 and .84, respectively. The Cronbach’s alpha

scores of Concentration, Self- Testing, Study Aids, and Time Management for the

“Self- Regulation” component obtain .86, .84, .73, and .85 respectively. Also, a test-

retest correlation of .88 was computed for the total instrument. There were

different approaches the author used to determine the validity of learning and study

strategies inventory: (1) The scale scores were compared to other tests or subscales

which are measuring related factors; (2) some scales were validated adjacent to

performance measures; and (3) the learning and study strategies inventory had

repeated tests of user validity (Weinstein & Palmer, 2002).

Procedure

All the participants were briefed about the guidelines in answering the

questionnaires. They were asked if they are willing to participate in the study by

answering a series of questionnaires. The participants were guided accordingly on

how they answered the forms: (1) The researcher gave the rationale of the study, (2)

read the questions carefully; (2) instructed that there are no right or wrong answers

for the questionnaires. The researcher informed the participants that the study

needs to get authentic answer for more accurate result. The participants were also

made aware that their answers will not affect their class standing in school and

failure to follow the guidelines will be forfeited on the participation in the study.

The researchers administered to the participants all the questionnaires during their

class time. The researchers then scored the questionnaires for each subscale. Each

participant was assigned with a call number used for the purpose of identifying and

recording all the instruments.

Data Analysis

Confirmatory Factor Analysis (CFA) was conducted to provide factor

validity of the A-SRL-S with the MSLQ and LASSI. A measurement model was

constructed composed of a three-factor model. There were seven indicators for the

A-SRL-S (Memory strategy, goal setting, self-evaluation, seeking assistance,

environmental structuring, learning responsibility, and planning and organizing),

five indicators for the MLSQ (values, expectancies, affective, cognitive and

metacognitive, and resource management), and three indicators for the LASSI

(skill, will, and self-regulation). The three latent constructs were intercorrelated to

establish factor convergence and construct validity. Significant parameter estimates

should be produced to establish the relationship among the latent constructs. The

components should have significant estimates as well in order to provide proofs of

inclusion of for their respective latent constructs.

The fit of the hypothesized four-factor model was assessed by examining

several fit indices including three absolute and one incremental fit index. The

minimum fit function chi-square, the root mean square error of approximation

(RMSEA), and the standardized root mean square residual (SRMR) are absolute fit

indices. The chi-square statistic (χ2

) assesses the difference between the sample



covariance matrix and the implied covariance matrix from the hypothesized model

(Fan, Thompson, & Wang, 1999). A statistically non-significant χ2

indicates

adequate model fit. Because the χ2

test is very sensitive to large sample sizes (Hu &

Bentler, 1995), additional absolute fit indices were examined. The RMSEA is

moderately sensitive to simple model misspecification and very sensitive to complex

model misspecification (Hu & Bentler, 1998). Hu and Bentler (1999) suggest that

values of .06 or less indicate a close fit. The SRMR is very sensitive to simple

model misspecification and moderately sensitive to complex model

misspecification (Hu & Bentler, 1998). Hu and Bentler (1998) suggest that

adequate fit is represented by values of .08 or less. In addition, two incremental fit

indices, the comparative fit index (CFI) and the Tucker-Lewis Index (TLI) were

examined. The CFI and the TLI are moderately sensitive to simple model

misspecification and very sensitive to complex model misspecification (Hu &

Bentler, 1998). Hu and Bentler (1998) recommend a cutoff of .95 or greater for

both the CFI and the TLI.

Results

The scores obtained from the three questionnaires were summarized

according to their factors. The seven scores were obtained from the A-SRL-S, five

scores for MSLQ, and three scores for the LASSI. Descriptive statistics were

reported including their internal consistency using Cronbach’s alpha.

The mean scores for the A-SRL-S were still within the confidence interval

level of the means in the previous study (see Magno, 2009). However, the standard

deviations for this sample are lower than the previous study. The Cronbach’s alpha

for the A-SRL-S are still within the same range (.70-.84). The reported means

scores of the MSLQ and LASSI had higher means for this sample as compared

with the previous samples in the study of Pintrich, Smith, Garcia, and McKeachie

(1991) and Weinstein and Palmer (2002).



Table 1

Means, Standard Deviations, Confidence Intervals, and Internal Consistency

N M Confidence Confidence SD Cronbach’s

-95.00% 95.00% alpha

A-SRL-S

Memory Strategy 755 2.80 2.76 2.84 0.56 .84

Goal-setting 755 2.83 2.78 2.88 0.69 .74

Self-evaluation 755 2.81 2.77 2.85 0.54 .82

Seeking Assistance 755 2.90 2.86 2.94 0.54 .71

Environmental

Structuring 755 2.84 2.79 2.89 0.66 .70

Learning Responsibility 755 2.95 2.90 2.99 0.64 .72

Planning and

Organizing 755 2.99 2.95 3.03 0.60 .71

MSLQ

Values 755 4.43 4.35 4.52 1.19 .92

Expectancies 755 4.33 4.25 4.41 1.12 .90

Affective 755 4.07 3.99 4.15 1.15 .80

Cognitive and

Metacognitive 755 4.33 4.25 4.40 1.03 .95

Resource Management 755 4.37 4.30 4.45 1.08 .93

LASSI

Skill 755 3.13 3.09 3.18 0.65 .75

Will 755 3.12 3.08 3.17 0.64 .74

Self-regulation 755 3.14 3.10 3.18 0.59 .72

To further establish the convergence of the factors of the A-SRL-S with the

MSLQ and the LASSI, Pearson correlation was conducted. The results of the

correlation showed that all coefficients are significant below .001 alpha levels. The

significant correlations indicate that convergence was attained among the factors of

A-SRL-S, MSLQ, and LASSI.

Three measurement models were constructed to determine which structure

best explains the relationship of the A-SRL-S with the MSLQ and LASSI.

The first measurement models include A-SRL-S combined with MSLQ factors and

this is structured in a two factor model. A second measurement model consisting

of a two-latent factor model where A-SRL-S was combined with LASSI factors

structured with MSLQ. And lastly, a three-factor model was constructed where the

A-SRL-S, MSLQ, and LASSI were placed as separate latent factors that are

correlated.



Table 2

Correlation Matrix for the A-SRL-S, MSLQ, and LASSI (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

A-SRL-S

(1) Memory Strategy --

(2) Goal-setting .60 --

(3) Self-evaluation .57 .57 --

(4) Seeking assistance .56 .58 .66 --

(5) Environmental

(6) Structuring .51 .43 .47 .50 --

(7) Learning

Responsibility .51 .46 .53 .58 .61 --

(8) Planning and

Organizing .46 .45 .56 .60 .51 .60 --

MSLQ

(9) Values .10 .17 .20 .24 .20 .30 .33 --

(10) Expectancies .15 .20 .21 .25 .20 .27 .30 .85 --

(11) Affective .12 .23 .19 .23 .10 .20 .19 .57 .63 --

(12) Cognitive and

Metacognitive .17 .20 .23 .30 .21 .30 .33 .77 .82 .64 --

(13) Resource

Management .15 .20 .20 .29 .19 .30 .34 .77 .80 .56 .87 --

LASSI

(14) Skill .35 .25 .30 .28 .26 .31 .27 .31 .33 .23 .30 .27 --

(15) Will .29 .29 .24 .25 .26 .30 .29 .36 .37 .29 .34 .35 .51 --

(16) Self-regulation .38 .27 .31 .31 .33 .35 .35 .35 .36 .26 .34 .32 .66 .57 --

Note. All correlation coefficients are significant at p<.001

The results show that the three-factor model is best fitting model indicting

further support for the convergence of A-SRL-S with the MSLQ and LASSI

(2

=473.97, df=87, RMSEA=.08, PGI=.93, GFI=.92, NFI=.94, CFI=.95, and

TLI=.95). What is common in all the three models are the significant paths of all

manifest variables and significant correlations among latent factors. However, in the

model where A-SRL-S was combined with the two other measures, the model did

not reach adequate fit. The second model where A-SRL-S was combined with the

LASSI, the SRMR (.09) showed adequate fit. It was also observed that the path

estimates of the factors of A-SRL-S increased when it was combined with the

LASSI factors in one latent construct. The third model also showed that the

relationship between A-SRL-S and LASSI is stronger (.47) than the relationship

between A-SRL-S and MSLQ (.35). This indicates that the A-SRL-S has some

degree of equivalence with the LASSI where both are strong indicators of learning

strategies.



Table 3

Comparison of Fit Indices

Model A-SRL-S + MSLQ

with LASSI (2 Factor

Model)

A-SRL-S + LASSI with

MSLQ (2 Factor Model)

3 Factor Model

2

2648.02 1052.99 473.97

Df 89 89 87

RMSEA .26 .13 .08

SRMR .20 .09 .04

AIC 3.39 1.47 .71

SBC 3.78 1.66 .92

BCCVI 3.60 1.48 .71

Discussion

The present study established the construct validity of the A-SRL-S with the

MSLQ and LASSI. This was done by first correlating the factors of the three scales

in a zero order correlation. Three measurement models were tested to determine

how the A-SRL-S is best related to MSLQ and LASSI.

It was found in the study that all subscales of the A-SRL-S, MSLQ, and

LASSI were significantly related. The low p values obtained in the correlation

coefficients indicate that there is a small chance that the correlations are influenced

with some random error. This further proved the relationship of each A-SRL-S

subscale with the other established two measures. This initial analysis proved

evidence about the similarity of the A-SRL-S with the two other established

measures.

In the zero order correlations, higher convergence is observed when the

subscales of the A-SRL-S are intercorrelated among each other. There is also

slightly higher correlation among the A-SRL-S subscales with the LASSI subscales

as compared to the MSLQ subscales. This shows there is a closer similarity

between A-SRL-S factors and LASSI factors. More specifically, the self-regulation

component of the LASSI (concentration, self-testing, study aids, and time

management) had stronger correlations with all factors of the A-SRL-S.

The results of the zero order correlation was further strengthened by the

results of the CFA. The CFA first showed that there was improvement in the fit of

the model (using SRMR, AIC, SBC, and BCCVI) when the A-SRL-S factors were

combined with the LASSI factors under one latent factor. Second, the path

estimates of the factors of A-SRL-S increased when joined with the factors of

LASSI under one latent variable. Third, the correlation of A-SRL-S and LASSI as

latent constructs was stronger (.47) in the three-factor model. These results

generally suggest that there is closer similarity and equivalence in the measurement

of self-regulation between the A-SRL-S and the LASSI. The kind of self-regulation

measured by the LASSI is approximated by the A-SRL-S.



Both the A-SRL-S and LASSI are strong indicators of specific learning

strategies. The A-SRL-S deals with six learning strategies that include memory

strategy, goal-setting, self-evaluation, seeking assistance, environmental structuring,

and planning and organizing. On the other hand, the LASSI’s skill and self-

regulation aspects also specify specific strategies such as information processing,

selecting main ideas, test strategies, concentration, self-testing, study aids, and time

management. These strategies are commonly used by students when studying

materials and preparing for exams. The manual of the LASSI even described

explicitly that “The LASSI scales related to the self-regulation component of

strategic learning are: Concentration, Self-Testing, Study Aids, and Time

Management” (p. 5). This was further supported in the present study where A-SRL-

S is strongly related to the LASSI scales. The LASSI scales cover self-regulation of

the “learning process by using time effectively, focusing attention and maintaining

concentration over time, checking to see if learning demands are met for a class, an

assignment or a test, and using study supports such as review sessions, tutors or

special features of a textbook” (p. 5) that are similar with the contexts covered in the

A-SRL-S. Examples of these scenarios in the A-SRL-S include taking notes, reading

aloud, making schedules, asking for assistance and feedback, checking ones

progress, avoiding distractions, and marking important concepts.

The similarity between the as A-SRL-S and the LASSI as evidenced in the

correlations and the CFA means that the A-SRL-S is a strong indicator of specific

aspects of learning strategy measured by the LASSI. These aspects include

measurement of how students study and learn and how they feel about studying and

learning (see Eldrege, 1990).

In the previous studies that developed the A-SRL-S, the validity of the tool

is constructed using only the scale without other measures. The present study now

used external and prior measures of self-regulation (such as the MSLQ and LASSI)

that provide proof to its precision in measurement. This step in the development of

the A-SRL-S is necessary to build evidence that the tool has some degree of

similarity in the constructs measured by other scales (such as the MSLQ and

LASSI).

Since the construct validity of the A-SRL-S is now established with the

LASSI and MSLQ, there is evidence about the precision of the A-SRL-S as a

measure of self-regulation in general with specific learning strategies. The

theoretical conception of self-regulation is clarified as a tool that covers aspects of

learning strategies and study skills. It is safe to assume that the A-SRL-S covers

specific learning strategies that help learners achieve specific learning goals.

Given the established construct validity of the A-SRL-S, the tool is

recommended to be used as alternative to the MSLQ and LASSI when academic

self-regulation is needed to be measured. The tool can provide evidence about

skills and characteristics of learners that will lead to better learning. The next step in

establishing the tool is to provide a predictive validity to determine is consequences

to learning that includes criterion such as students’ achievement and other learning

strategies and metacognition.



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About the Author

Dr. Carlo Magno is presently a faculty of the Counseling and Educational

Psychology Department of De La Salle University, Manila. His research interest

includes self-regulation, learning strategies, student achievement, metacognition,

and language learning. Further correspondence can be addressed to him at

[email protected]

Special thanks to Ms. MR Aplaon for the help in gathering literature reviews and

my educational psychology students for the assistance in the data gathering.

mailto:[email protected]

validating the academic self-regulated learning scale with the motivated strategies for learning...

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